Stock investment strategy combining earnings power index and machine learning
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Jun, So Young | - |
dc.contributor.author | Kim, Dong Sung | - |
dc.contributor.author | Jung, Suk Yoon | - |
dc.contributor.author | Jun, Sang Gyung | - |
dc.contributor.author | Kim, Jong Woo | - |
dc.date.accessioned | 2023-06-01T06:57:33Z | - |
dc.date.available | 2023-06-01T06:57:33Z | - |
dc.date.created | 2022-10-06 | - |
dc.date.issued | 2022-12 | - |
dc.identifier.issn | 1467-0895 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/185819 | - |
dc.description.abstract | We propose an intermediate-term stock investment strategy based on fundamental analysis and machine learning. The approach uses predictors from the Earnings Power Index (EPI) as input variables derived from cross-sectional and time-series data from a company's financial statements. The analytical methods of machine learning allow us to validate the link between financial factors and excess returns directly. We then select stocks for which returns are likely to increase at the time of the next disclosed financial statement. To verify the proposed approach's usefulness, we use company data listed publicly on the Korean stock market from 2013 to 2019. We examine the profitability of trading strategy based on ten machine-learning techniques by forming long, short, and hedge portfolios with three different measures. As a result, most portfolios, including EPI-related variables, present positive returns regardless of the period. Especially, the neural network of the two layers with sigmoid function presents the best performance for the period of 3 months and 6 months, respectively. Our results show that incorporating machine learning is useful for mid-term stock investment. Further research into the possible convergence of financial statement analysis and machine-learning techniques is warranted. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | Elsevier Inc. | - |
dc.title | Stock investment strategy combining earnings power index and machine learning | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Jung, Suk Yoon | - |
dc.contributor.affiliatedAuthor | Jun, Sang Gyung | - |
dc.contributor.affiliatedAuthor | Kim, Jong Woo | - |
dc.identifier.doi | 10.1016/j.accinf.2022.100576 | - |
dc.identifier.scopusid | 2-s2.0-85138051303 | - |
dc.identifier.wosid | 000883790000001 | - |
dc.identifier.bibliographicCitation | International Journal of Accounting Information Systems, v.47, pp.1 - 35 | - |
dc.relation.isPartOf | International Journal of Accounting Information Systems | - |
dc.citation.title | International Journal of Accounting Information Systems | - |
dc.citation.volume | 47 | - |
dc.citation.startPage | 1 | - |
dc.citation.endPage | 35 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | ssci | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Business & Economics | - |
dc.relation.journalWebOfScienceCategory | Business | - |
dc.relation.journalWebOfScienceCategory | Business, Finance | - |
dc.relation.journalWebOfScienceCategory | Management | - |
dc.subject.keywordPlus | FINANCIAL STATEMENT ANALYSIS | - |
dc.subject.keywordPlus | FUNDAMENTAL ANALYSIS | - |
dc.subject.keywordPlus | PREDICTING STOCK | - |
dc.subject.keywordPlus | CAPITAL-MARKETS | - |
dc.subject.keywordPlus | CROSS-SECTION | - |
dc.subject.keywordPlus | SELECTION | - |
dc.subject.keywordPlus | CLASSIFICATION | - |
dc.subject.keywordPlus | INFORMATION | - |
dc.subject.keywordPlus | RETURNS | - |
dc.subject.keywordPlus | EQUILIBRIUM | - |
dc.subject.keywordAuthor | Earnings prediction | - |
dc.subject.keywordAuthor | Stock price forecast | - |
dc.subject.keywordAuthor | Machine learning | - |
dc.subject.keywordAuthor | Intermediate-term investment | - |
dc.identifier.url | https://www.sciencedirect.com/science/article/pii/S1467089522000288?via%3Dihub | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
222, Wangsimni-ro, Seongdong-gu, Seoul, 04763, Korea+82-2-2220-1365
COPYRIGHT © 2021 HANYANG UNIVERSITY.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.